Distributed Energy Resources Electric Profile Identification in Low Voltage Networks Using Supervised Machine Learning Techniques
Increasing integration of distributed energy resources (DER) in the electrical network has led distribution network operators to unprecedented challenges. This issue is compounded by the lack of monitoring infrastructure on the low voltage (LV) side of distribution networks at residential and utilit...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10049841/ |
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| author | Andres F. Moreno Jaramillo Javier Lopez-Lorente David M. Laverty Paul V. Brogan Santiago H. Hoyos Velasquez Jesus Martinez-Del-Rincon Aoife M. Foley |
| author_facet | Andres F. Moreno Jaramillo Javier Lopez-Lorente David M. Laverty Paul V. Brogan Santiago H. Hoyos Velasquez Jesus Martinez-Del-Rincon Aoife M. Foley |
| author_sort | Andres F. Moreno Jaramillo |
| collection | DOAJ |
| description | Increasing integration of distributed energy resources (DER) in the electrical network has led distribution network operators to unprecedented challenges. This issue is compounded by the lack of monitoring infrastructure on the low voltage (LV) side of distribution networks at residential and utility sides. Non-intrusive load monitoring (NILM) methods provide an opportunity to add value to conventional electric measurements and to increase the observability of LV networks for the implementation of active management network techniques and intelligent control of DER. This work proposes a novel implementation of NILM methods for the identification of DER electrical signatures from aggregated measurements taken at the LV side of a distribution transformer. The implementation evaluates three machine learning algorithms such as k Nearest Neighbours (kNN), random forest and a multilayer perceptron under 100 scenarios of DER integration. A year of minutely reported values of electric current, voltage, active power, and reactive power are used to train and test the proposed model. The <inline-formula> <tex-math notation="LaTeX">$F_{1}$ </tex-math></inline-formula> scores achieved of 73% and 93% for Electrical Vehicles (EV) and rooftop photovoltaic (PV) respectively and processing times below <inline-formula> <tex-math notation="LaTeX">$314~\mu \text{s}$ </tex-math></inline-formula> on an Intel Core i7-8700 machine. These results confirm the relevance of the NILM method based on low frequency electric measurements from the real-time identification of DER. |
| first_indexed | 2024-04-10T05:16:05Z |
| format | Article |
| id | doaj.art-91a20af2830e4e09960c3581108c73a8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T05:16:05Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-91a20af2830e4e09960c3581108c73a82023-03-09T00:00:05ZengIEEEIEEE Access2169-35362023-01-0111194691948610.1109/ACCESS.2023.324797710049841Distributed Energy Resources Electric Profile Identification in Low Voltage Networks Using Supervised Machine Learning TechniquesAndres F. Moreno Jaramillo0https://orcid.org/0000-0003-1952-8465Javier Lopez-Lorente1https://orcid.org/0000-0003-0032-1149David M. Laverty2https://orcid.org/0000-0002-5697-0546Paul V. Brogan3Santiago H. Hoyos Velasquez4Jesus Martinez-Del-Rincon5https://orcid.org/0000-0002-9574-4138Aoife M. Foley6https://orcid.org/0000-0001-6491-2592School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast, U.KDepartment of Electrical and Computer Engineering, FOSS Research Centre for Sustainable Energy, PV Technology Laboratory, University of Cyprus, Nicosia, CyprusSchool of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast, U.KData Applications, Phasora Ltd., Belfast, U.KEnergEIA Research Cluster, EIA University, Envigado, Antioquia, ColombiaSchool of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast, U.KSchool of Mechanical and Aerospace Engineering, Queen’s University Belfast, Belfast, U.KIncreasing integration of distributed energy resources (DER) in the electrical network has led distribution network operators to unprecedented challenges. This issue is compounded by the lack of monitoring infrastructure on the low voltage (LV) side of distribution networks at residential and utility sides. Non-intrusive load monitoring (NILM) methods provide an opportunity to add value to conventional electric measurements and to increase the observability of LV networks for the implementation of active management network techniques and intelligent control of DER. This work proposes a novel implementation of NILM methods for the identification of DER electrical signatures from aggregated measurements taken at the LV side of a distribution transformer. The implementation evaluates three machine learning algorithms such as k Nearest Neighbours (kNN), random forest and a multilayer perceptron under 100 scenarios of DER integration. A year of minutely reported values of electric current, voltage, active power, and reactive power are used to train and test the proposed model. The <inline-formula> <tex-math notation="LaTeX">$F_{1}$ </tex-math></inline-formula> scores achieved of 73% and 93% for Electrical Vehicles (EV) and rooftop photovoltaic (PV) respectively and processing times below <inline-formula> <tex-math notation="LaTeX">$314~\mu \text{s}$ </tex-math></inline-formula> on an Intel Core i7-8700 machine. These results confirm the relevance of the NILM method based on low frequency electric measurements from the real-time identification of DER.https://ieeexplore.ieee.org/document/10049841/Distributed energy resources (DER)non-intrusive load monitoring (NILM)low voltage networks distribution networks (LVN)random forestsupervised machine learning |
| spellingShingle | Andres F. Moreno Jaramillo Javier Lopez-Lorente David M. Laverty Paul V. Brogan Santiago H. Hoyos Velasquez Jesus Martinez-Del-Rincon Aoife M. Foley Distributed Energy Resources Electric Profile Identification in Low Voltage Networks Using Supervised Machine Learning Techniques IEEE Access Distributed energy resources (DER) non-intrusive load monitoring (NILM) low voltage networks distribution networks (LVN) random forest supervised machine learning |
| title | Distributed Energy Resources Electric Profile Identification in Low Voltage Networks Using Supervised Machine Learning Techniques |
| title_full | Distributed Energy Resources Electric Profile Identification in Low Voltage Networks Using Supervised Machine Learning Techniques |
| title_fullStr | Distributed Energy Resources Electric Profile Identification in Low Voltage Networks Using Supervised Machine Learning Techniques |
| title_full_unstemmed | Distributed Energy Resources Electric Profile Identification in Low Voltage Networks Using Supervised Machine Learning Techniques |
| title_short | Distributed Energy Resources Electric Profile Identification in Low Voltage Networks Using Supervised Machine Learning Techniques |
| title_sort | distributed energy resources electric profile identification in low voltage networks using supervised machine learning techniques |
| topic | Distributed energy resources (DER) non-intrusive load monitoring (NILM) low voltage networks distribution networks (LVN) random forest supervised machine learning |
| url | https://ieeexplore.ieee.org/document/10049841/ |
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